Pixel Binning Readout for Image Sensor Resolution Trade-offs
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Solution Overview
Problem
Conventional image sensors face a trade-off between spatial resolution, frame rate, and power consumption, where achieving high resolution images results in lower frame rates and increased power consumption, as they read out the entire pixel array.
Innovation Solution
The method and apparatus implement pixel binning and readout by determining a region of interest and a region of non-interest within an image frame, reading out each row in the region of interest at high resolution and combining pixels in the region of non-interest to form a low resolution region, thereby reducing power consumption and increasing frame rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire pixel array is read out to obtain high resolution images, then spatial resolution is improved, but frame rate decreases and power consumption increases
Solution Approach 1:
The pixel array is segmented into multiple regions of interest (ROIs) and non-ROI areas. Different readout strategies are applied to different segments: high-resolution readout for ROI regions and binned/combined readout for non-ROI regions. This segmentation allows the system to achieve high spatial resolution where needed while maintaining higher frame rates overall by reducing the total number of pixels that require full-resolution readout.
Solution Approach 2:
Different quality levels are applied to different regions of the image. Regions of interest receive full high-resolution readout to maintain measurement precision, while non-ROI regions are binned or combined to reduce data volume. This local quality approach ensures that spatial resolution is optimized specifically in areas where it is most needed, rather than uniformly across the entire array.
2Measurement precision
If the entire pixel array is read out to obtain high resolution images, then spatial resolution is improved, but power consumption increases
Solution Approach 1:
The pixel array is segmented into multiple regions of interest (ROIs) and non-ROI areas. Different readout strategies are applied to different segments: high-resolution readout for ROI regions and binned/combined readout for non-ROI regions. This segmentation allows the system to achieve high spatial resolution where needed while maintaining higher frame rates overall by reducing the total number of pixels that require full-resolution readout.
Solution Approach 2:
Different quality levels are applied to different regions of the image. Regions of interest receive full high-resolution readout to maintain measurement precision, while non-ROI regions are binned or combined to reduce data volume. This local quality approach ensures that spatial resolution is optimized specifically in areas where it is most needed, rather than uniformly across the entire array.
3Productivity
If pixels are combined in the region of non-interest to form low resolution region, then power consumption is reduced and frame rate increases, but spatial resolution decreases in that region
Solution Approach 1:
Different quality levels are applied to different regions of the image. Regions of interest receive full high-resolution readout to maintain measurement precision, while non-ROI regions are binned or combined to reduce data volume. This local quality approach ensures that spatial resolution is optimized specifically in areas where it is most needed, rather than uniformly across the entire array.
Data Source
AI summary
Various embodiments of the present technology may comprise a method and apparatus for pixel binning and readout. The method and apparatus may determine a region of interest and a region of non-interest within an image frame according to a detected feature. In various embodiments, the method and apparatus may readout each row in the region of interest resulting in a high resolution region and combine the pixels within the region of non-interest resulting in a low resolution region.